DAT unit test

README.md

@@ -117,7 +117,7 @@ If EasyRec is useful for your research, please cite:
 
 ### Join Us
 
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 - Email Group: [email protected].
 

easy_rec/python/compat/early_stopping.py

@@ -21,9 +21,9 @@
 import os
 import threading
 import time
-from distutils.version import LooseVersion
 
 import tensorflow as tf
+from distutils.version import LooseVersion
 from tensorflow.python.framework import dtypes
 from tensorflow.python.framework import ops
 from tensorflow.python.ops import init_ops

easy_rec/python/model/rank_model.py

@@ -158,7 +158,9 @@ def build_rtp_output_dict(self):
               'failed to build RTP rank_predict output: classification model ' +
               "expect 'probs' prediction, which is not found. Please check if" +
               ' build_predict_graph() is called.')
-      elif loss_types & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
+      elif loss_types & {
+          LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS
+      }:
         if 'y' in self._prediction_dict:
           forwarded = self._prediction_dict['y']
         else:
@@ -379,7 +381,9 @@ def _build_metric_impl(self,
           metric.recall_at_topk.topk)
     elif metric.WhichOneof('metric') == 'mean_absolute_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
+      if loss_type & {
+          LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS
+      }:
         metric_dict['mean_absolute_error' +
                     suffix] = metrics_tf.mean_absolute_error(
                         label, self._prediction_dict['y' + suffix])
@@ -391,7 +395,9 @@ def _build_metric_impl(self,
         assert False, 'mean_absolute_error is not supported for this model'
     elif metric.WhichOneof('metric') == 'mean_squared_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
+      if loss_type & {
+          LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS
+      }:
         metric_dict['mean_squared_error' +
                     suffix] = metrics_tf.mean_squared_error(
                         label, self._prediction_dict['y' + suffix])
@@ -403,7 +409,9 @@ def _build_metric_impl(self,
         assert False, 'mean_squared_error is not supported for this model'
     elif metric.WhichOneof('metric') == 'root_mean_squared_error':
       label = tf.to_float(self._labels[label_name])
-      if loss_type & {LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS}:
+      if loss_type & {
+          LossType.L2_LOSS, LossType.SIGMOID_L2_LOSS, LossType.ZILN_LOSS
+      }:
         metric_dict['root_mean_squared_error' +
                     suffix] = metrics_tf.root_mean_squared_error(
                         label, self._prediction_dict['y' + suffix])

easy_rec/python/test/train_eval_test.py

@@ -7,11 +7,11 @@
 import threading
 import time
 import unittest
-from distutils.version import LooseVersion
 
 import numpy as np
 import six
 import tensorflow as tf
+from distutils.version import LooseVersion
 from tensorflow.python.platform import gfile
 
 from easy_rec.python.main import predict
@@ -1286,6 +1286,12 @@ def test_xdeefm_backbone_on_taobao(self):
         self._test_dir)
     self.assertTrue(self._success)
 
+  @unittest.skipIf(gl is None, 'graphlearn is not installed')
+  def test_dat_on_taobao(self):
+    self._success = test_utils.test_single_train_eval(
+        'samples/model_config/dat_on_taobao.config', self._test_dir)
+    self.assertTrue(self._success)
+
 
 if __name__ == '__main__':
   tf.test.main()

requirements/docs.txt

@@ -5,4 +5,4 @@ recommonmark==0.6.0
 sphinx==5.1.1
 sphinx_markdown_tables==0.0.17
 sphinx_rtd_theme
-tensorflow-probability==0.11.0
+tensorflow-probability==0.11.0

samples/model_config/dat_on_taobao.config

@@ -19,7 +19,7 @@ train_config {
   }
   save_checkpoints_steps: 4000
   sync_replicas: false
-  num_steps: 100000
+  num_steps: 100
 }
 
 eval_config {
@@ -129,7 +129,6 @@ data_config {
 
   label_fields: 'clk'
   batch_size: 512
-  num_epochs: 10000
   prefetch_size: 32
   input_type: CSVInput
 

setup.cfg

@@ -10,7 +10,7 @@ multi_line_output = 7
 force_single_line = true
 known_standard_library = setuptools
 known_first_party = easy_rec
-known_third_party = absl,common_io,docutils,eas_prediction,faiss,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,scipy,six,sklearn,sparse_operation_kit,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,tensorflow_probability,yaml
+known_third_party = absl,common_io,distutils,docutils,eas_prediction,faiss,future,google,graphlearn,kafka,matplotlib,numpy,oss2,pai,pandas,psutil,scipy,six,sklearn,sparse_operation_kit,sphinx_markdown_tables,sphinx_rtd_theme,tensorflow,tensorflow_probability,yaml
 no_lines_before = LOCALFOLDER
 default_section = THIRDPARTY
 skip = easy_rec/python/protos